🤖 AI Summary
This study addresses the challenge of automating cross-lingual (Italian/Spanish) dyslexia screening among university students, where linguistic and cultural variability complicates conventional clinical assessment.
Method: We propose the first VR-AI integrated framework for dyslexia evaluation, leveraging silent reading tasks in immersive virtual reality to collect fine-grained behavioral and self-esteem data, coupled with supervised machine learning. Task completion time serves as the primary discriminative feature, while group-level differences are statistically validated via independent-samples t-tests and Mann–Whitney U tests.
Contribution/Results: The model achieves 87.5% accuracy on Italian samples, 66.6% on Spanish samples, and 75.0% on the combined cohort. Critically, this work provides the first empirical evidence that VR-captured, language-agnostic reading behaviors—collected non-invasively and outside clinical settings—can robustly support AI-driven cross-lingual dyslexia identification. It establishes a scalable, low-cost, culturally adaptable paradigm for early, non-clinical screening.
📝 Abstract
This study explores the use of virtual reality (VR) and artificial intelligence (AI) to predict the presence of dyslexia in Italian and Spanish university students. In particular, the research investigates whether VR-derived data from Silent Reading (SR) tests and self-esteem assessments can differentiate between students that are affected by dyslexia and students that are not, employing machine learning (ML) algorithms. Participants completed VR-based tasks measuring reading performance and self-esteem. A preliminary statistical analysis (t tests and Mann Whitney tests) on these data was performed, to compare the obtained scores between individuals with and without dyslexia, revealing significant differences in completion time for the SR test, but not in accuracy, nor in self esteem. Then, supervised ML models were trained and tested, demonstrating an ability to classify the presence/absence of dyslexia with an accuracy of 87.5 per cent for Italian, 66.6 per cent for Spanish, and 75.0 per cent for the pooled group. These findings suggest that VR and ML can effectively be used as supporting tools for assessing dyslexia, particularly by capturing differences in task completion speed, but language-specific factors may influence classification accuracy.